Abstract: A completely edible pet treat is releasably attachable to another object, such as a camera or a mobile device having a camera. The pet treat includes a 100% edible fastening member, shaped and sized to be releasably attached to the object, and a 100% edible remaining member integral with the fastening member, sized to be visible to a pet, when the pet treat is attached to the object.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: A new game and the methods thereof are disclosed. The game comprises utilizing new devices, rules, methods, well-defined terms, and a plurality of playing cards designed with special markings and performing alternative actions within a conditional loop to tactically and strategically form the cards on-hand as required melds/pair with bonus-earning patterns. The structure, format, rules, and playing methods of the present invention are designed to provide a great mental workout, and the purpose of the present invention is to engage people and create a friendly and gregarious environment that allows everyone to enjoy the company of others.

Abstract: There is provided p-type organic polymers of general formula I. The polymers may be useful as semi-conducting material. Thus, thin films and devices comprising such polymers are also provided.

Abstract: There is provided p-type organic polymers of general formula I. The polymers may be useful as semi-conducting material. Thus, thin films and devices comprising such polymers are also provided.

Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.

Abstract: There is provided p-type organic polymers of general formula I. The polymers may be useful as semi-conducting material. Thus, thin films and devices comprising such polymers are also provided.

Abstract: A new game and the methods thereof are disclosed. The game comprises utilizing new devices, rules, methods, well-defined terms, and a plurality of playing cards designed with special markings and performing alternative actions within a conditional loop to tactically and strategically form the cards on-hand as required melds/pair with bonus-earning patterns. The structure, format, rules, and playing methods of the present invention are designed to provide a great mental workout, and the purpose of the present invention is to engage people and create a friendly and gregarious environment that allows everyone to enjoy the company of others.

Abstract: A network processing device calculates variable link metrics and then prioritizes selection of network links for sending packets according to the calculated variable link metrics. The variable link metrics can include a link capacity index that represents a combination of platform and interface capabilities for nodes on opposite ends of the network links. The link metrics can also include an expected retransmission value that indicates the percentage of packets that may have to be transmitted over different links.

Abstract: A network processing device calculates variable link metrics and then prioritizes selection of network links for sending packets according to the calculated variable link metrics. The variable link metrics can include a link capacity index that represents a combination of platform and interface capabilities for nodes on opposite ends of the network links. The link metrics can also include an expected retransmission value that indicates the percentage of packets that may have to be transmitted over different links.

Abstract: An all-CMOS output buffer drives a bus that can operate at 3 volts and 5 volts. When in a high-impedance state, the output buffer draws little or no current. If the bus is driven to 5 volts by an external device, the high impedance output buffer is in danger of latch-up and distortion of the bus logic level since it only has a 3-volt power supply and does not use a charge pump or an extra 5-volt supply. A biasing circuit couples an N-well that contains p-channel transistors and a driver transistor to the bus driven to 5 volts. Thus the N-well is also driven to 5 volts, the voltage on the bus. The gate of the p-channel driver transistor in the high-impedance output buffer is also coupled to the N-well by another p-channel transistor, raising the gate potential to 5 volts. Thus the gate and body of the p-channel driver transistor is at 5 volts, eliminating reversing current and latch-up problems. A transmission gate isolates the gate of the p-channel driver transistor from the rest of the device's circuitry.